Find Academic Supervisors

• Security and Privacy
• Reverse Engineering
• Machine Learning
• +5 more

Michael Pucher is a dedicated researcher affiliated with the Faculty of Computer Science at Vienna University of Technology, where he contributes to the Research Group Security and Privacy. Currently serving as a Visiting Researcher (October 2023 to February 2024), his work bridges academic research and practical security challenges in computing systems. He holds a Diplom-Ingenieur (Dipl.-Ing.) degree, equivalent to a Master of Science in Engineering, and a Bachelor of Science (BSc), reflecting a strong foundation in technical disciplines. Dr. Pucher's research spans the critical domains of computer security and privacy, with a focus on reverse engineering, obfuscation techniques, binary analysis, and the application of machine learning to security problems. His investigations delve into integrated circuit analysis, software protection mechanisms, and the development of resilient methods for identifying semantic functionality in obfuscated programs. Analysis of his 2022 publications reveals a cohesive research trajectory emphasizing innovative solutions for security challenges. Key themes include the integration of machine learning for clone detection in binaries, simulation-based approaches to counter obfuscation, and advanced image processing techniques for hardware reverse engineering, indicating a multidisciplinary approach that merges hardware and software security. Active in the academic community, Dr. Pucher has contributed through teaching engagements, peer review for conferences such as the IEEE Workshop on Offensive Technologies, and presentations at international venues including the IEEE Physical Assurance and Inspection of Electronics (PAINE) workshop.

Amirali Khodadadian GostarView profile

Senior Lecturer

• Machine Learning
• Data Analytics
• Multi-Object Tracking
• +5 more

Dr. Amirali Khodadadian Gostar is a Senior Lecturer at the School of Engineering, RMIT University. His research focuses on machine learning, data analytics, multi-object tracking, sensor management, and data-driven manufacturing. He has contributed to advancements in autonomous systems, anomaly detection, and multi-agent coordination through projects like distributed information fusion for connected vehicles and geometrically-informed tracking algorithms. Research Interests : Image processing, sensor fusion, robotics, control systems, and industrial automation. Key Projects : Development of electronic pre-tension systems for seatbelts, AI-driven logistics optimization, and stereo vision systems for defect detection. Publications : Over 60 peer-reviewed articles in top journals/conferences such as IEEE Transactions on Intelligent Transportation Systems and ISA Transactions, focusing on tracking algorithms, anomaly detection, and multi-agent systems. His work bridges theoretical frameworks (e.g., random finite set theory) with practical applications in robotics, transportation, and manufacturing. He actively supervises PhD/Master's students on topics ranging from quantum AI in logistics to eye gaze tracking for visual attention modeling.

Hamza AlkhatibView profile

Researcher

• Bayesian Modelling
• Monte Carlo Methods
• Uncertainty Modelling
• +5 more

Hamza Alkhatib is a Senior Researcher at the Geodetic Institute within the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover. His research focuses on advanced computational methods in geodetic data analysis, including Bayesian modeling, Monte Carlo simulations, uncertainty quantification, and filtering techniques for multi-sensor systems. He leads projects funded by DFG, BfG, and BMVI, addressing topics like hydrographic monitoring, cooperative sensor networks, and machine learning applications in geodesy. Alkhatib holds a PhD from the University of Bonn (2007) and a Habilitation from Leibniz University Hannover (2020). Education & Career : PhD (2007): University of Bonn, Dissertation on Monte Carlo methods for satellite gravity missions Habilitation (2020): Leibniz University Hannover, Advanced methods in geodetic data analysis Senior Research Staff since 2019 Research Interests : Bayesian inference and robust statistical methods Filtering algorithms (Kalman, Particle Filters) for dynamic systems Machine learning for geodetic point cloud analysis and damage detection Sensor fusion and multi-sensor positioning systems Hydrographic monitoring and marine infrastructure assessment Key Projects : DFG-funded adaptive robust adjustment of geodetic measurement processes Research Training Group GRK 2159 on dynamic sensor networks WaMUT project for hydrographic water change zone monitoring 3D HydroMapper for port infrastructure inspection Awards : DAAD Award for Outstanding Achievements (2000) Syrian Ministry of Academic Education Award (1998) Teaching : Teaches courses on geodetic data analysis, recursive state estimation, and machine learning applications at both bachelor and master levels.

• Signal Processing
• Robotics
• Biomedical Engineering
• +5 more

Salviano F. S. P. Soares is an Assistant Professor at the Engineering Department within the Science and Technology School at UTAD (Portugal), and a researcher at IEETA – Institute of Electronics and Telematics Engineering, University of Aveiro. His research focuses on signal processing, robotics, biomedical engineering, IoT systems, and their applications in healthcare, education, and agriculture. He has led industry-funded projects with Altice Labs and Sonae, specializing in R&D with FCT grants. His work spans embedded systems design, human-robot collaboration, precision agriculture, and educational robotics. Notable contributions include Arduino-based robotics for STEM education, predictive maintenance systems, and AI-driven human activity recognition. Publications highlight advancements in autonomous systems, medical signal processing (ECG/EMG), and sensor networks. Collaborations include developing tools like the Kid Grígora robot for early computational thinking and the FEROX simulator for industrial human-robot synergy. He also pioneers smart agriculture solutions using LoRaWAN for vineyard monitoring and IoT-based sensor architectures for UAV inspections. His work integrates machine learning with traditional engineering challenges, addressing topics like fault detection in industrial robots, telemedicine systems, and cognitive architectures for elderly care. He actively publishes in IEEE, journals, and international conferences, emphasizing practical applications of theoretical research.

• Robotics for Extreme Environments
• Machine Learning for Decision Making
• Robot Interoperability
• +3 more

Robert Skilton is a Visiting Professor of Practice at the University of Oxford's Oxford Robotics Institute, affiliated with the Department of Engineering Science. He holds a PhD from the University of Surrey and has extensive experience in robotics for extreme environments, particularly in nuclear and fusion settings. Previously, he served as Head of Robotics Research at the UK Atomic Energy Authority (UKAEA) and led national/international robotics programs. Education: BSc (2006) Computer Science, University of Reading; MSc (2011) Cybernetics, University of Reading; PhD (2023) Generative AI for Industrial Inspection, University of Surrey. Research focuses on robotics for extreme environments, including tele-operation, autonomous inspection, and nuclear decommissioning. Key interests include robot system agility, interoperability, and machine learning for decision-making. He leads projects like UK Fusion Robotics (EPSRC-funded) and Lasers4MaaS, alongside collaborations with institutions like Korea's tendon-driven inspection robotics initiative (IRIS). Recent articles emphasize fusion reactor structural integrity, radiation-resistant polymers, Fukushima Daiichi debris retrieval, and bilateral teleoperation performance. His work bridges robotics theory with practical nuclear energy challenges. Grants and projects include EPSRC funding, international fusion robotics collaborations, and development of the Generic Nuclear Robotics Architecture (GNRA). He advises on DPhil opportunities in robotics for fusion energy and nuclear decommissioning.

• Robotics
• Nonlinear Control
• System Identification
• +5 more

Professor Ian Manchester is a faculty member in the Department of Mechatronic Engineering at the University of Sydney. He holds a B.Eng (Electrical) Honours and a PhD from the University of New South Wales. His academic roles include Director of the Australian Centre for Robotics and Director of the Australian Robotic Inspection & Asset Management Hub (ARIAM). He has held leadership positions such as Director of Research at the Australian Centre for Field Robotics and co-Director of the Sydney Institute for Robotics and Intelligent Systems. His research focuses on robotics, nonlinear control, system identification, and machine learning, with applications in biomedical engineering, forestry, mining automation, and aviation. He has supervised numerous PhD students working on projects like acrobatic legged robots, surgical robotics, and robotic asset management. Recent publications emphasize robust control methodologies, nonlinear observer design, and stable machine learning models. His work often bridges theoretical advances with practical applications, such as SLAM algorithms and dynamics of walking robots. Prof Manchester has received the Sydney Research Accelerator Fellowship (SOAR) in 2019. He serves on editorial boards for IEEE journals and international conferences. His labs and collaborations include the Australian Centre for Robotics and the ARIAM Hub, focusing on robotic inspection and asset management solutions.

• Robotics
• Control Systems
• Aerial Vehicles
• +3 more

Rita Cunha is an Associate Professor at the Institute of Systems and Robotics (ISR) within the University of Lisbon. Her academic role includes teaching courses such as Introduction to Robotics, Signals and Systems, and Control Engineering. Her research focuses on advanced robotics and control systems, with particular emphasis on multirotor drones, formation control, trajectory planning, and autonomous systems. She has contributed significantly to the development of control strategies for unmanned aerial vehicles (UAVs), sensor-based navigation, and distributed multi-agent systems. Her work integrates theoretical control methodologies with practical applications, addressing challenges in aggressive drone maneuvers, collision avoidance, and sensor network deployment for wildfire surveillance. She leads research in collaborative robotics, including multi-drone cinematography and cooperative load transportation systems. Her contributions span both algorithm design and experimental validation, leveraging tools like the Pegasus simulator and physics-informed neural networks. Key Research Areas: Autonomous Systems, Aerial Robotics, Formation Control, Trajectory Optimization, Sensor Networks Teaching: Introduction to Robotics, Control Systems, Signals and Systems Labs/Teams: ISR (Institute of Systems and Robotics) - active in UAV control and autonomous systems research Her over 50 publications since 2013 demonstrate sustained innovation in robotics control, with recent emphasis on high-order consensus algorithms, adaptive control under uncertainties, and real-time trajectory generation for confined spaces. Current projects include LiDAR-based autonomous inspection systems and distributed mission execution frameworks for aerial cinematography.

Rasha AlshawiView profile

Assistant Professor

• Deep Learning
• Computer Vision
• Natural Language Processing
• +3 more

Rasha Alshawi is an Assistant Professor in the MS in Data Science program at the University of San Francisco, specializing in deep learning and computer vision. Her research focuses on AI-driven solutions for infrastructure monitoring and public safety, including automated inspection of underground systems like culverts, sewers, and DC motors. She has collaborated with the U.S. Army Corps of Engineers on floodwater control structure projects. Education: PhD (in progress) in Engineering and Applied Science at University of New Orleans (2025), MS in Information Technology from Middle Technical University (2017). Research interests include semantic segmentation, transformer-based NLP models, dataset curation, and AI applications in urban planning. She has held roles as a Research Assistant at Gulf States Center for Environmental Informatics and Teaching Assistant at University of New Orleans. Publications emphasize advanced neural network architectures like DAU-Net and SHARP-Net for infrastructure defect detection, imbalance-aware segmentation techniques, and sinkhole identification using multiscale filters.

Christophoros NikouView profile

Professor

• Image Processing
• Computer Vision
• Pattern Recognition
• +12 more

Christophoros Nikou is a Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. He holds a Ph.D. in Image Processing and Computer Vision from Université Louis Pasteur - Strasbourg I, France (1999), a DEA in Photonics and Image Processing from the same institution (1995), and a Diploma in Electrical Engineering from Aristotle University of Thessaloniki, Greece (1994). His research interests include: Image Processing Computer Vision Pattern Recognition Biomedical Image Analysis Medical Imaging Machine Learning His recent publications (2022–2024) reflect a strong trend toward deep learning, generative models, and multimodal analysis, particularly in document image analysis, super-resolution, and biomedical applications. He has made significant contributions to visual tracking, segmentation, and image registration, with recent work extending into virtual reality, hypercomplex neural networks, and model compression. Scientific awards include: Best paper award, IAPR/IEEE International Conference on Biometrics (ICB’16), 2016 Best paper award, Bayesian and Graphical Models for Biomedical Imaging (BAMBI’16), 2016 He has supervised numerous students, many of whom have co-authored publications with him, and his research has been supported by collaborations across multiple institutions. He has also co-edited a book on biomedical image processing and contributed to several book chapters. His work spans both theoretical and applied domains, with consistent publication in top-tier IEEE and Springer venues.

Gabriel SpadonView profile

Assistant Professor

• Spatiotemporal Analytics
• Time-Series Forecasting
• Complex Network Mining
• +13 more

Gabriel Spadon is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the MAPS Lab (Modeling and Analytics on Predictive Systems), focusing on spatiotemporal analytics, network science, and machine learning. His work is part of the Big Data Analytics, AI & Machine Learning research cluster. Education: PhD in Computer Science and Computational Mathematics, University of São Paulo (USP), 2021 MSc in Computer Science and Computational Mathematics, University of São Paulo (USP), 2017 BSc in Computer Science, São Paulo State University (UNESP), 2015 Research Interests: Gabriel Spadon’s research lies at the intersection of data science, machine learning, and geoinformatics . He specializes in spatiotemporal forecasting , complex network mining , and trajectory modeling , particularly in maritime and urban environments. His work integrates physics-informed AI, graph-based learning, and environmental data to improve predictive accuracy and decision support. Key domains include vessel movement prediction, environmental monitoring, and urban intelligence. Recent Publication Trends: His latest publications (2024–2025) focus on maritime mobility , using AIS data and physics-informed neural networks (PINNs) for vessel trajectory forecasting. He applies deep learning, clustering, and probabilistic fusion to model complex, multi-modal movement patterns. Other themes include fishing activity detection, port network analysis, and AI for public health and climate matching. Scientific Awards: Advising and Grants: Dr. Spadon supervises multiple graduate students and research assistants in the MAPS Lab, working on mobility data mining, physics-informed AI, and geospatial analytics. His research is supported by major grants from the Ocean Frontier Institute (OFI) , Canadian Space Agency (CSA) , Mitacs , and IMT Atlantique . He leads projects such as smartWhales , AISViz , and Physics-Informed Mobility Forecasting , involving international collaborations with institutions in Brazil and France. Labs and Teams: He is the Director of the MAPS Lab , which develops cutting-edge computational methods for predictive systems. The lab collaborates with the smartWhales initiative (DHI, WSP, Fisheries and Oceans Canada), MERIDIAN , and the Institute for Big Data Analytics . His team includes PhD and MCS students working on AI, data mining, and signal processing for real-world ocean and urban challenges.

Gordon DobieView profile

Professor

• Ultrasonics
• Non-Destructive Evaluation (NDE)
• Robotics
• +7 more

Professor Gordon Dobie is a full-time faculty member in the Department of Electronic & Electrical Engineering within the Faculty of Engineering at the University of Strathclyde. He holds the academic rank of Professor and is actively engaged in research, teaching, and supervision of PhD students. MEng in Electrical and Mechanical Engineering with Distinction, University of Strathclyde (2000–2005) PhD in Ultrasonic Sensor Platforms for Non-Destructive Evaluation, University of Strathclyde (2006–2009) His research focuses on applied engineering solutions in ultrasonics, non-destructive evaluation (NDE), robotics, automation, and embedded systems. Key areas include automated inspection, signal processing, computer vision, and photogrammetry, with applications in structural integrity and medical imaging. His work supports industrial and medical advancements through real-time, intelligent inspection systems. The recent publications reflect a strong trend in integrating robotics and sensor technologies for robust inspection systems, particularly in challenging environments such as pipelines and medical diagnostics. The research combines ultrasonic sensing with computer vision, machine learning, and tactile feedback, demonstrating interdisciplinary innovation in engineering for safety-critical applications. Professor Dobie has received recognition through active engagement in major research projects and collaborations, including KTP partnerships and Innovate UK-funded initiatives. While specific awards are not listed, his role as Principal Investigator on multiple high-impact projects highlights his leadership and contribution to technological innovation. He advises several research students and leads or contributes to numerous grants, including projects such as RAICo - Next Generation Remote Inspection of Confined Spaces and KTP - Cokebusters , focusing on improving ultrasonic inspection pigs. His work is supported by strong industrial partnerships and contributes to UN Sustainable Development Goals through technological advancement. He is affiliated with the Centre for Ultrasonic Engineering (CUE) and participates in external collaborations with institutions such as the National Robotarium. His research group works on developing next-generation inspection systems using mobile robotics, sensor fusion, and AI-driven data analysis.

• Computer Vision
• Robotics
• Underwater Exploration
• +4 more

Rafael Garcia Campos is a tenured Professor at the University of Girona 's Department of Architecture and Computer Technology, with a career spanning 20+ years. He serves as Director of the Computer Vision and Robotics Institute (VICOROB) and leads the Underwater Vision Lab. His work bridges computer vision , robotics , and marine exploration , with leadership roles in European projects like iToBoS and DeeperSense. Active in 3D optical mapping , autonomous underwater vehicles , and deep learning applications, he has received grants from H2020, MINECO, and FP7. PhD in Computer Engineering (University of Girona, 2001) MSc in Computer Engineering (University Autònoma de Barcelona, 1994) Research focuses on underwater imaging , autonomous navigation , and computer vision algorithms for seafloor exploration , coral reef monitoring , and medical imaging . His publications emphasize real-time perception , sensor fusion , and 3D reconstruction in challenging environments like polarized underwater scenes and defective point clouds. Grant highlights include: iToBoS Project (2021-2024): €1.14M (Coordinator) DeeperSense (2021-2023): €475K SIREC (2021-2024): €249K He has delivered keynote lectures globally on topics like underwater perception and autonomous marine systems , and co-authored books on 3D scene modeling and underwater mosaicing. His lab techniques have been commercialized through contracts with institutions like the University of Miami and Nova Southeastern University.

• Robust Statistics
• Feature Selection
• Data Visualization
• +6 more

Garth Tarr is Associate Professor in Statistics and Data Science at the School of Mathematics and Statistics, University of Sydney. As Program Director for the Master of Data Analytics, he leads educational innovation in flexible delivery and student engagement. Current research focuses on robust statistics, feature selection, and agricultural data science. Sydney Institute of Agriculture member Statistical Society of Australia Australian Mathematical Society His research interests center on robust statistical methods for complex data, with applications in meat science and biostatistics. Key projects include: Outlier identification with functional data Finite sample robust estimation Improved model averaging techniques Statistical issues in meat quality evaluation Recent software development includes the mplot R package for model selection visualization. Teaching contributions span units like Experimental Design and Applied Time Series Analysis. Collaborations extend to Meat and Livestock Australia, Teys Australia, and international institutions like Maastricht University. Notable scientific awards include multiple Vice-Chancellor’s Awards for Teaching Excellence (2021, 2023), EJG Pitman Prize (2012), and Golden Jubilee Travel Grant (2013). Supervision includes active PhD candidates Rajan Shankar and Martin Huang working on modern regularization techniques and sparse modeling.

• Geomatics
• Computer Vision
• Remote Sensing
• +2 more

Vincenzo Di Pietra is a fixed-term researcher at the Department of Environmental, Land, and Infrastructure Engineering (DIATI) within the Politecnico di Torino (Polytechnic University of Turin) , where he works on geomatics and remote sensing technologies. He is a member of the PIC4SeR Interdepartmental Center for Service Robotics and holds a Learning to Teach (L2T) certification. His research focuses on UAV photogrammetry, GNSS positioning, indoor navigation, and geomatics applications for infrastructure monitoring. Academic Affiliation : College of Environmental and Land Engineering (member), College of Civil and Building Engineering (member), College of Electronic/Telecommunications/Physics Engineering (invited member) Di Pietra's work integrates computer vision , machine learning , and sensor fusion to develop innovative solutions for 3D reconstruction , structural health monitoring , and rapid mapping . Recent publications highlight applications of deep learning for corrosion detection, UWB-GNSS hybrid systems, and multi-sensor terrain modeling. Awards : AUTeC Award (2021), Quality Award (2018), SIFET Young Authors Award (2017) He supervises PhD students Milad Bagheri and Myrta Maria Macelloni in Civil and Environmental Engineering . Di Pietra teaches courses on Statistical Data Processing , Geomatics , and Interdisciplinary Projects , while leading research projects like MORPHEUS (2023-2025) for resilient landscape monitoring and commercial contracts with Rai Way and OrigoSat .

• Field Robotics
• Underwater Robotics
• Autonomous Navigation
• +5 more

Eleni Kelasidi is a Professor in Field Robotics at the Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), and holds a part-time position as Senior Research Scientist and Head of SINTEF ACE-RoboticLab at SINTEF Ocean. She leads interdisciplinary research bridging robotics and aquaculture, with a focus on autonomous systems operating in dynamic environments. Her education includes: MSc in Electrical and Computer Engineering (University of Patras, 2009) PhD in Engineering Cybernetics (NTNU, 2015) Kelasidi's research explores nonlinear control, hydrodynamic modeling of swimming robots, robotic vision, and autonomous navigation. She pioneered robotic applications in aquaculture, establishing the first dedicated robotics lab for this domain. Her work addresses energy efficiency, obstacle avoidance, and real-time decision-making for underwater systems. Her recent publications emphasize adaptive path planning, aquaculture robotics, and robust autonomy in uncertain environments, reflecting a trend toward AI-integrated underwater operations and industry-driven solutions. Awards and recognitions: FRIPRO-Young Researcher Talent (2020) SINTEF’s Outstanding Research Award (2024) She leads multiple projects including FRIPRO-Young Researcher Talent and EU initiatives, and founded the Autonomous and Robotic Aquaculture Systems Lab (SINTEF ACE-RoboticLab). Kelasidi collaborates with MIT, ETH Zurich, and industry partners to advance robotic solutions for aquaculture, focusing on net pen inspection, biofouling prevention, and precision fish farming.